{"id":"W1567859499","doi":"10.7202/012844ar","title":"Technologies de l’information, productivité et croissance des entreprises : résultats basés sur de nouvelles microdonnées internationales","year":2006,"lang":"fr","type":"article","venue":"L Actualité économique","topic":"Economic Growth and Productivity","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Humanities; Political science; Philosophy","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004407407,0.0006180869,0.000957288,0.004338228,0.0007902465,0.002858917,0.0006220553,0.0007025352,0.009171118],"category_scores_gemma":[0.01199005,0.0003647894,0.001753538,0.009692279,0.001074361,0.002042702,0.001606344,0.00110953,0.001497374],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00246322,"about_ca_system_score_gemma":0.00232523,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06324904,"about_ca_topic_score_gemma":0.07418735,"domain_scores_codex":[0.9969696,0.0008356491,0.000221668,0.0004984895,0.001206128,0.0002685103],"domain_scores_gemma":[0.9684406,0.02118671,0.003701867,0.001375467,0.004794178,0.0005012537],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001914031,0.001072997,0.684371,0.005518456,0.001730328,0.0007205845,0.02075346,0.007802757,0.004711869,0.008596665,0.005277467,0.2575305],"study_design_scores_gemma":[0.00007348567,0.001795707,0.8934404,0.001393308,0.001403137,0.0004007244,0.03080611,0.004192017,0.006738653,0.002304357,0.05731551,0.0001366047],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9598275,0.009035018,0.004474565,0.0003168951,0.00003005863,0.0002253046,0.005074255,0.00007450383,0.02094191],"genre_scores_gemma":[0.9665874,0.007241144,0.006967504,0.0001616215,0.00002780969,0.0003680029,0.004705113,0.00008232952,0.01385908],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06324904,"threshold_uncertainty_score":0.1257618,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03125192606848529,"score_gpt":0.2139492597603899,"score_spread":0.1826973336919046,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}